False Data Injection Attacks Against State Estimation in Power Distribution Systems

Deng, Ruilong; Zhuang, Peng; Liang, Hao

doi:10.1109/tsg.2018.2813280

Cited by 175 publications

(86 citation statements)

References 29 publications

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“…Concepts such as Micro-Grid (MG), Smart Grid (SG) and Smart Island (SI) in AC and DC power systems with advances in digital communication technologies have received more attention in the past few years. Recently, several researchers and studies have highlighted the vulnerability of traditional AC and DC state estimators to the FDIAs in that an adversary is able to present manipulated measurements to the mislead operation systems [11]. Some kind of attacks are able to bypass common Bad Data Detection (BDD) in the State Estimation (SE) as the measurement remnants with FDIAs are the same as the measurement residuals with no FDIAs.…”

Section: A Backgroundmentioning

confidence: 99%

“…By assuming that the change vector of the SV x is determined as c, the residuals of the system measurement before and after the attack would be and . In general, natural measurements z can cross the LNR-based BDD module, by calculating the 2 − of the residual measurement in order to detect bad measurements, we can define equation (11):…”

Section: Main Construction Of Ac-fdiasmentioning

confidence: 99%

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False Data Injection Attack Detection based on Hilbert-Huang Transform in AC Smart Islands

et al. 2020

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In Smart Island (SI) systems, the operators of power distribution system usually utilize actual-time measurement information as the Advanced Metering Infrastructure (AMI) to have accurate, efficient, advanced control and monitoring. SI system can be vulnerable to complicated information integrity attacks such as False Data Injection Attack (FDIA) on some equipment including sensors and controllers, which can generate misleading operational decision in the system. Today, lack of detailed research in the evaluation of power system that links the FDIAs with system stability is felt, and it will be important for both assessment of the effect of cyber-attack and taking preventive protection measures. In this regards, time-frequency-based differential approach is proposed for SI cyber-attack detection according to non-stationary signal assessment. In this paper, non-stationary signal processing approach of Hilbert-Huang Transform (HHT) is performed for the FDIA detection in several case studies. Since various critical case studies with a small FDIA in data where accurate and efficient detection can be a challenge, the simulation results confirm the efficiency of HHT approach. In this research, the configuration of the SI test case is developed in the MATLAB software with several Distributed Generations (DGs). As a result, it is found that the HHT approach is completely efficient and reliable for FDIA detection target in AC-SI. The simulation results verify that the proposed model is able to achieve accuracy rate of 93.17%.

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Section: A Backgroundmentioning

confidence: 99%

Section: Main Construction Of Ac-fdiasmentioning

confidence: 99%

False Data Injection Attack Detection based on Hilbert-Huang Transform in AC Smart Islands

et al. 2020

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“…The malicious measurement za could bypass the BDD detector and not lead to a change in the residual value under the condition a = h( x + c) − h( x), where c = [c 1 , · · · , cn] is the maliciously injected error on the system state [19]. After false data injection, distribution system state estimation will get an erroneous system state [16]:…”

Section: Problem Formulationmentioning

confidence: 99%

“…However, the integrity of state estimation is under mounting threat, and they are vulnerable to cyber-attacks specifically false data injection attacks [7]. Deng et al [16] extend FDI attacks against state estimation in transmission systems to distribution feeders. These attacks can cause severe damages to the distribution systems.…”

Section: Introductionmentioning

confidence: 99%

Benchmark of machine learning algorithms on capturing future distribution network anomalies

Mohammadpourfard

Weng

Tajdinian

2019

IET Generation, Transmission & Distribution

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The conventional distribution network is undergoing structural changes and becoming an active grid due to the advent of smart grid technologies encompassing distributed energy resources (DERs), aggregated demand response and electric vehicles (EVs). This establishes a need for state estimation (SE)-based tools and real-time monitoring of the distribution grid to correctly apply active controls. Although such new tools may be vulnerable to cyber-attack, the cyber-security of distribution grid has not received enough attention. As smart distribution grid intensively relies on communication infrastructures, we assume in this paper that an attacker can compromise the communication and successfully conduct attacks against crucial functions of distribution management system, making the distribution system prone to instability boundaries for collapses. We formulate attack detection problem in the distribution grid as a statistical learning problem and demonstrate a comprehensive benchmark of statistical learning methods on various IEEE distribution test systems. The proposed learning algorithms are tested using various attack scenarios which include distinct features of modern distribution grid such as integration of DERs and EVs. Furthermore, the interaction between the transmission and distribution systems and its effect on the attack detection problem is investigated. Simulation results show attack detection is more challenging in the distribution grid.

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“…Following the local attack scheme in [28], Deng et al in [52] proposed an attack model against distribution system state estimation. Specifically, the authors discussed the strategy to avoid having the complete knowledge of the network topology and related parameters.…”

Section: Other Local Attack Modelsmentioning

confidence: 99%

Intelligent data attacks against power systems using incomplete network information: a review

Song

Liu

et al. 2018

J. Mod. Power Syst. Clean Energy

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With the integration of information technologies, power system operations are increasingly threatened by cyber-attacks. It has even been revealed that an attacker can inject false data into real-time measurements stealthily without knowing the full configuration (e.g., network topology) of a power system. In this paper, we present a comprehensive review on false data injection attacks which utilize barrier conditions, blind identification techniques and data driven approaches to overcome limitations of incomplete network information. We also point out future research topics for facilitating the detection and prevention of such false data attacks.

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False Data Injection Attacks Against State Estimation in Power Distribution Systems

Cited by 175 publications

References 29 publications

False Data Injection Attack Detection based on Hilbert-Huang Transform in AC Smart Islands

False Data Injection Attack Detection based on Hilbert-Huang Transform in AC Smart Islands

Benchmark of machine learning algorithms on capturing future distribution network anomalies

Intelligent data attacks against power systems using incomplete network information: a review

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